The human-associated microbiota has recently been established as a critical determinant of health and disease. It has attracted wide interest across medical disciplines with the promise of novel microbiota-targeted therapies that can effectively shift the microbiota toward a health-promoting state. The microbiota is noted for its exceptional complexity, with intricate metabolic networks governing microbial symbiotic and competitive interactions. The simultaneous use of multiple ?omics technologies for characterizing the microbiota has been recognized broadly by the research community as a powerful approach because it can expose the interactions between the various components of the microbiota and link microbiota composition and function, a key requirement of targeted therapy development. However, the success of this approach depends on the development of computational and statistical methods for identifying the high-order interactions in the microbiota that are relevant for influencing disease risk and for bridging multiple high-dimensional ?omics data streams. We aim to address this methodological gap by developing, evaluating, applying and distributing a new set of tools for performing meaningful analysis and integration of ?omics data for human microbiota studies. We will frame the development of our tools around two of the most powerful and widely-used technologies for characterizing the human-associated microbiota: (1) sequence-based microbial taxonomic profiling for characterizing microbial community composition and (2) spectroscopy-based untargeted metabolomic profiling for characterizing microbial community function. We will first develop and evaluate a method for integrating multi-omic data streams that leverages publicly available databases of microbial metabolic pathways. Next, we will develop a method for mapping associations between composition and clinical outcomes through the lens of microbiota functional profiles. We will then apply our methods for identifying microbial communities and their phenotypes associated with clinical endpoints in a large cohort. Finally, our methods will be released to the human microbiota research community as an open-source software package. The work we propose will build analytic tools that perform functional integration of multiple ?omics data streams and evaluate complex relationships with clinical outcomes. This represents a novel framework for identifying microbiota-health associations and their functional underpinnings in a manner that embraces the complexity of this system. The significance of this work lies in its potential to help translate experimental and human subjects studies of the microbiota into targeted therapies that shift the microbiota toward a health-promoting state.